The present invention relates to analyzing social interactions, and more specifically, this invention relates to analyzing dynamic interaction graphs that represent social interactions.
With the increasing prevalence of social networking services and the proliferation of mobile devices in everyday life, Internet-based social interactions between people have never been so common. These social interactions can often be represented as a dynamic interaction graph, where new interactions are continuously ingested over time and represented as edges within such graphs. As a result, systems to extract timely insights from dynamically changing graphs are highly desirable.
Existing models for analyzing dynamic interaction graphs are generally divided into one of two models—the snapshot model or the sliding window model.
The snapshot model treats all interactions within the graph equally regardless of age. Because all interactions are maintained within the snapshot model, the size of the graph is unbounded, which can present processing and storage issues. Additionally, because the size of the graph is unbounded and may grow to massive portions, important recent interactions may become lost within the graph.
The sliding window model considers all graph interactions that occur within a specified recent time window. As a result of omitting from analysis graph interactions that have occurred outside of the specified time window, interactions may be abruptly removed from the analysis For example, such a sliding window model may remove from analysis all interactions beyond a two day, three week, one year, etc., window. Consequently, due to the lost continuity, important past relationships may be undesirably removed from analysis.
Accordingly, by analyzing graph interactions utilizing either the snapshot model or the sliding window model, it may be impossible to satisfy both recency and continuity requirements.